|
other query modes : |
Identifier query |
Coordinate query |
Criteria query |
Reference query |
Basic query |
Script submission |
TAP |
Output options |
Object types |
Help |
2004ApJS..152..201G - Astrophys. J., Suppl. Ser., 152, 201-209 (2004/June-0)
Automated classification of 2000 bright IRAS sources.
GUPTA R., SINGH H.P., VOLK K. and KWOK S.
Abstract (from CDS):
An artificial neural network (ANN) scheme has been employed that uses a supervised back-propagation algorithm to classify 2000 bright sources from the Calgary database of Infrared Astronomical Satellite (IRAS) spectra in the region 8-23 µm. The database has been classified into 17 predefined classes based on the spectral morphology. We have been able to classify over 80% of the sources correctly in the first instance. The speed and robustness of the scheme will allow us to classify the whole of the Low Resolution Spectrometer database, containing more than 50,000 sources, in the near future.
Abstract Copyright: ∼
Journal keyword(s): Infrared: Galaxies - Methods: Data Analysis
VizieR on-line data: <Available at CDS (J/ApJS/152/201): table3.dat classes.dat>
Simbad objects: 386
To bookmark this query, right click on this link: simbad:2004ApJS..152..201G and select 'bookmark this link' or equivalent in the popup menu